Posted on December 15, 2020.

With businesses collecting troves of new data (and likely storing a significant amount of old data), they run into issues of managing and using it to their advantage. Much of this has to do with rapidly advancing and emerging technology; it can quickly become overwhelming for businesses trying to be more efficient and discreet in using their data.

Another hurdle businesses face? Managing to stay ahead of emerging trends and know the best options for their business. That’s why Sanity Solutions gathered 20 significant enterprise data management trends from 2020, and what we see on the horizon for the year to come.

1. Cloud Computing

Cloud computing continues to provide a lot of advantages for businesses. The adoption of a multi-cloud topology continues to progress. Growing Kubernetes adoption allows applications to be extremely mobile. That mobility leads to a two-way street of applications moving to the public cloud and being pulled back into a private cloud.

Security is tightening as companies like VMware add to their capabilities for protecting an organization’s infrastructure. Machine learning is being injected into nearly all aspects of the IT infrastructure. Enhanced insights are proving to be a real game changer for companies understanding their infrastructures.

2. Augmented Data Management

Vendors are adding ML capabilities and AI engines to make self-configuring and self-tuning processes pervasive. This helps businesses reduce manual data management considerably.

These processes automate many manual tasks and allow users with less technical skills to be more autonomous when using data. Gartner predicts, “Through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management.”

3. Machine Learning Infrastructure

Similar to AI, Machine Learning (ML) has had a reputation of being too complicated for most businesses, but the payoff is almost priceless. Automated tools powered by machine learning can be powerful in data management because they extract information and compile it into easily digestible visual formats. Productivity ML tools are being inserted into most cloud-based SaaS offerings or offered as a stand-alone cloud-based architecture that provides ease of implementation and utilization.

4. Graph Databases

Graph databases can define patterns and correlations via mathematical structures used to model pairwise relations between objects. Graph databases can maintain better performance over a large dataset versus traditional relational databases. Graph databases consist of nodes or data points that are connected through links. These links or commonalities are persistent between nodes. For retailers, this can be beneficial in pinpointing consumer behaviors and buying patterns. Graph databases perform differently than their relational counterparts

5. Data Security

Data security is always a top trend in data management, as businesses prioritize data integrity and minimize breaches and data loss risks. Whether on-premises or in-hybrid multi-cloud environments, secure data solutions help you gain greater visibility and insights to investigate and remediate threats. They also enforce real-time controls and adhere to the myriad of compliance requirements such as GDPR, PCI, HIPAA, or SOX. Security infrastructure based on SIEM, SOAR, and SASE creates highly automated detection and response architectures that arm security teams with the latest advancements in defending the enterprise.

6. Artificial Intelligence (AI)

Artificial intelligence today is properly known as narrow or weak AI, in that it is designed to perform a narrow or specific task.  A few examples of narrow AI include Google search, image recognition software, personal assistants like Siri and Alexa, and self-driving cars. AI performs frequent, high-volume, computerized tasks reliably and without fatigue. AI finds structure and regularities in data to acquire a skill, turning the algorithm into a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can also teach itself what product to recommend next online. Over time, the models adapt when given new data.

7. Persistent Memory (PMEM)

Businesses demand better performance as applications based on ML or AI perform in real time. Maintaining more data closer to the processor and in a persistent state provides invaluable benefits such as greater throughput and reduced latency. PMEM, initially developed in a joint venture by Intel and Micron, allows Intel based servers to expand their memory footprint up to 4 TB in capacity. This provides an incredible boost in performance for in-memory databases or metadata stored locally. PMEM provides the fastest storage media, far exceeding NVMe drives.

8. Natural Language Processing

Natural language processing (NLP) is the interaction of human language and technology. Well known examples of natural language processing are voice activated technologies like Alexa, Siri, and Google Home. NLP serves as the front-end to an Artificial Intelligence backend where voice requests are translated into actionable results. As NLP continues to grow, so does the gap in voice search data. With voice searches expected to make up 50% of all searches, businesses must get on board. NLP helps by processing and collecting voice-based data, and it can also function internally for those who need to access data via voice orders.

9. Augmented Analytics

Augmented analytics uses enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms. The heavy lifting of manually sifting through vast volumes of complex data (due to lack of skills or time constraints) is significantly reduced as the analysis is automated and can be set to run continuously. Augmented data preparation brings data together from multiple sources much faster. Algorithms can be used to detect schemas and joins, repetitive transformation and integrations can be fully automated, data quality and enrichment recommendations are auto-generated by the system, and you can even automate the profiling.

10. Embedded Analytics

Those who understand the world of analytics also understand how time-consuming and inefficient translating data can be. Embedded analytics is a digital workplace capability where data analysis occurs within a user’s natural workflow without toggle to another application. Moreover, embedded analytics tends to be narrowly deployed around specific marketing campaign optimization, sales lead conversions, inventory demand planning and financial budgeting. With embedded analytics, your business can reduce the amount of time analytics teams spend converting data into readable insights for everyone. That means they can do what they do best: analyze data to form solutions and strategies. It even allows employees to work with data and easy-to-read visualizations for overall better decision-making.

11. Predictive Analytics

Predictive analytics leverages numerous techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to predict future events or behaviors. Predictive analytics models capture relationships among many factors to assess risk with a particular set of conditions or criteria. Predictive analytics allows organizations to become proactive, forward-looking, anticipating outcomes and behaviors based on the data rather than assumptions. Predictive analytics automates complex decisions to make predictions or outcomes.

12.  Democratization

Democratization of technology refers to providing people with easy access to technical or business expertise without extensive (and costly) training. It focuses on four key areas — application development, data and analytics, design and knowledge — and is often referred to as “citizen access,” which has led to the rise of citizen data scientists, citizen programmers, and more. 

Many professionals believe data democratization is a game-changer. When you allow data access to any tier of your company, it empowers individuals at all levels of ownership and responsibility to use the data in their decision making.

13. Data Visualization

Data visualization is a graphical representation of information and data. Visual elements like charts, graphs, and maps data visualization tools provide an accessible way to see and understand trends, outliers, and data patterns. Data visualization helps to tell stories by curating data into a form easier to understand, highlighting the trends. A good visualization tells a story, removing the noise from data, and highlighting the useful information.

Data can only be so powerful if you know how to read, interpret, and explain it. It’s not just about the what, but the why. By presenting data visually, you can provide better context and form easy-to-understand narratives for those you’re sharing it with.

14. Hyperautomation

Hyperautomation refers to applying advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. In simple terms, hyper-automation refers to the mixture of automation technologies to augment and expand human capabilities. 

Hyperautomation doesn’t just automate your data; it automates decisions. Hyper-automation does not only refer to implementing tools to manage tasks. It requires collaboration between humans, as well. This is because humans are vital decision-makers and can use the technology to interpret data and apply logic. 

15. Decision Intelligence

Decision intelligence is a trending field that contains a range of decision-making methods to design, model, align, execute, and track decision models and processes. The implementation offers a structure for organizational decision-making and processes with the integration of machine learning algorithms.

There are three primary levels of decision models:

  1. Human-Based Decisions
  2. Machine-Based Decisions
  3. Hybrid-Based Decisions

This 2020 trend combines the best of decision management and decision support. In other words, it allows decision analysts (also an emerging trend) to design, monitor, and execute processes that ultimately impact business outcomes and behaviors.

16. Decision Analysts

The decision analyst is responsible for developing a model or providing an analysis result based on complex data. It can be challenging for leadership (or others who are more hands-off in day-to-day processes) to understand the burgeoning quantity of data produced daily. Decision analysts assist decision-makers by bringing structure, data, and proven methods to the decision-making process. Success is based on informing and improving the organizational decision process for the leadership team.

17. Data Marketplaces

Data marketplaces are on the rise as we speak. In fact, 35% of large organizations will become buyers or sellers of data by 2022. This is done through formal online data marketplaces that consolidate third-party data offerings. Data Marketplace’s gives data scientists, business intelligence and analytics professionals, and everyone who desires data-driven decision-making access to live and ready-to-query data from both your internal ecosystem of business partners and customers, and from potentially thousands of data providers and data service providers.

18. Internet of Things

Internet of Things (IoT) devices are pieces of hardware embedded into phones, equipment, medical devices, and more typically performing monitoring duties. These pieces of hardware typically include wireless sensors, actuators and ruggedized computers. These devices collect information and transmit it to a central point for aggregation. This data is transformed and mined to produce alerting, data insights and trending that can be used to reduce costs, increase efficiencies, and forecast future opportunities for businesses.

19. Blockchain Technologies

Blockchain is just a chain of blocks (Digital Information), stored in a public database (the “chain”). The blocks store information such as who is participating, metadata regarding a transaction and unique identifiers.  The infrastructure supporting blockchain functions is a highly distributed architecture that is highly performant and, most importantly, positively secures based on individual blocks assigned a unique hash applied at the transaction time.  Blockchain technologies can supplement businesses’ existing data management infrastructure. They can provide the framework to build a comprehensive, highly secure architecture for real-time transactions.

20. Working with Data Management Experts

Technology is ever-changing, making it difficult for businesses to learn new trends — let alone keep up with them. Working with experts who act as an extension of your team helps you seamlessly incorporate data management solutions into your own business. At Sanity Solutions, we do just that, providing tailored solutions to you and your unique goals.

When you’re ready to find the right data solutions for your business, Sanity Solutions is ready to help.

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